MK5104
Marketing
Analytics
Dr. Michał Folwarczny
michal.folwarczny@universityofgalway.ie
University
ofGalway.ie
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Unit 11
1. Group Projects
2. Marketing insights from
survey data
3. Case study: Coca-Cola
marketing survey insights
4. Survey data analysis in
Jamovi
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Marketing insights from
survey data
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What is
survey
data?
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???
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Another example
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How much do you disagree (1) or agree (5)
with the statements about the
ChestCharm below:
Strongly
disagree
Rather
disagree
Neither
disagree
nor agree
Rather
agree
Strongly
agree
ChestCharm is tasty
ChestCharm appeals to my paletes
I enjoy
eating ChestCharm
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ChestCharm is delicious
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ChestCharm is tasty
● Is it necessary to run four
separate analyses?
ChestCharm appeals to my paletes
● What if the survey has a lot of
questions, e.g., 45 items?
ChestCharm is delicious
I enjoy eating ChestCharm
● First of all, what are you trying
to learn about customers?
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Aren’t all these
questions related
to taste?
ChestCharm is tasty
ChestCharm appeals to my paletes
I enjoy eating ChestCharm
ChestCharm is delicious
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Questions we’ll address today:
1. How to reduce data for more parsimonious analysis
2. Why it is still recommended to have multiple items in
surveys, even if we are interested only in one variable (e.g.,
attitude, taste, brand loyalty)
3. What factor analysis is and why it is a fundamental skill
for analysing survey data
4. What to report if we wish to reduce data for meaningful
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analyses
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Reliability in survey data
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What is the Scale Reliability?
The consistency of a set of measurements or measuring
instrument, often used to assess the quality of the
measurement.
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Types of reliability
Test-Retest Reliability: Stability of test scores over time.
Inter-Rater Reliability: Consistency of scores assigned by
different observers.
Internal Consistency (Reliability): Consistency of
performance across items within a test.
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What is Cronbach's Alpha (α)?
The most commonly used measure of internal consistency
(reliability).
The role of Cronbach's Alpha in assessing the reliability of
scales.
Ranges from 0 to 1.
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α greater than or equal to .90 = excellent*
α greater than or equal to .80 = good reliability
α greater than or equal to .70 = acceptable reliability
α greater than or equal to .60 = questionable reliability
α greater than or equal to .50 = poor reliability
α glower than .50 = unacceptable reliability
*Greater = better?
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● Cronbach alpha should not be overused (it does not say
much about a scale)
● The measure has recently been criticized
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Source:
Dunn, T. J., Baguley, T., & Brunsden, V. (2014). From alpha to omega: A practical solution toofGalway.ie
the
pervasive problem of internal consistency estimation. British Journal of Psychology, 105(3), 399-412.
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An introduction to Factor Analysis (FA) for survey data reduction
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What is factor analysis (FA)?
A statistical method used to describe variability among
observed, correlated variables in terms of a potentially lower
number of unobserved variables called factors (aka latent
variables).
FA is used to identify underlying relationships between
variables and reduce data to a more manageable size
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without
losing significant information.
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Types of Factor Analysis
Exploratory Factor Analysis (EFA): Used when the
underlying structure among variables is unknown.
Confirmatory Factor Analysis (CFA): Used to test the
hypothesis that a relationship between observed variables and
their underlying latent constructs exists.
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Factor Loadings: Correlations between observed variables
and factors, indicating the strength of the relationship.
Eigenvalues: Measure of variance in all variables which can
be attributed to the factor.
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Source: Folwarczny, M., Li, N. P., Sigurdsson, V., Tan, L. K., & Otterbring, T. (2021). Development and psychometric
University of the Anticipated Food Scarcity Scale (AFSS). Appetite, 166, 105474.
evaluation
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Source: Folwarczny, M., Li, N. P., Sigurdsson, V., Tan, L. K., & Otterbring, T. (2021). Development and psychometric
University of the Anticipated Food Scarcity Scale (AFSS). Appetite, 166, 105474.
evaluation
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Source: Folwarczny, M., Li, N. P., Sigurdsson, V., Tan, L. K., & Otterbring, T. (2021). Development and psychometric
University of the Anticipated Food Scarcity Scale (AFSS). Appetite, 166, 105474.
evaluation
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Source: Folwarczny, M., Li, N. P., Sigurdsson, V., Tan, L. K., & Otterbring, T. (2021). Development and psychometric
University of the Anticipated Food Scarcity Scale (AFSS). Appetite, 166, 105474.
evaluation
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ofGalway.ie
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Source: Folwarczny, M., Li, N. P., Sigurdsson, V., Tan, L. K., & Otterbring, T. (2021). Development and psychometric
University of the Anticipated Food Scarcity Scale (AFSS). Appetite, 166, 105474.
evaluation
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ofGalway.ie
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Source: Folwarczny, M., Li, N. P., Sigurdsson, V.,
Tan, L. K., & Otterbring, T. (2021). Development
and psychometric evaluation of the Anticipated
Food Scarcity Scale (AFSS). Appetite, 166, 105474.
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Dos and Don’ts of a Good Marketing Survey Design
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✅ Use a Consistent Number of Scale Points
Best practice: 5-point, 6-point or 7-point scales
(e.g., Strongly Disagree → Strongly Agree).
Avoid too few (e.g., 3-point) or too many (e.g., 22-point) options.
✅ Label Key Points Clearly
Example: 1 = Very Dissatisfied, 3 = Neutral, 5 = Very Satisfied
Avoid just using numbers without explanations.
✅ Balance the Scale to Reduce Bias
Ensure equal positive and negative options.
Example (Balanced):
Very Poor – Poor – Neutral – Good – Very Good
✅
Use Consistent Direction in Your Surveys
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If
"1" means "Very Dissatisfied" in one question, don't flip it in another.
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✅ Ask One Thing at a Time
●
●
Bad Example: "How satisfied are you with our product quality and customer service?"
Fix: Split into two separate questions.
✅ Use Clear, Simple Language
●
●
Bad: "How would you evaluate the efficacy of our customer interaction touchpoints?"
Fix: "How helpful was our customer service?"
✅ Ensure Neutral & Unbiased Wording
●
●
Bad: "How much do you love our new product?"
Fix: "How would you rate our new product?"
✅ Include a 'Prefer Not to Answer' Option (Only) When Needed
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Helps avoid forced answers on sensitive topics.
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❌ Avoid Double Negatives
●
●
Bad: "Do you disagree that our website is not difficult to use?"
Fix: "Is our website easy to use?"
❌ Don’t Use Leading Questions
●
●
Bad: "Most people love our product. How satisfied are you?"
Fix: "How satisfied are you with our product?"
❌ Avoid Assumptions in Questions
● Bad: "How often do you use our app?" (Assumes the respondent uses it.)
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● Fix: "Do you use our app? [Yes/No]" followed by "How often?"
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General Instructions for a Consumer Survey
Thank you for taking the time to participate in our survey! Your feedback is
valuable and will help us improve our products and services.
Please read each question carefully and select the response that best reflects
your opinion. There are no right or wrong answers—we are interested in your
honest thoughts and experiences.
The survey should take approximately [X] minutes to complete. Your responses
will remain confidential and will only be used for research purposes.
If you have any questions, please feel free to contact [support email or phone
number].
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Click ‘Next’ to begin.
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In the following section, you will see a series of statements related to your
experience with our product/service. Please indicate your level of agreement or
satisfaction using the provided scale.
Example:
●
●
Strongly Disagree (1) → Strongly Agree (7)
Very Dissatisfied (1) → Very Satisfied (7)
Please select the option that best reflects your opinion.
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How likely you are to recommend our product to
others?
✅ 5-Point Scale
1 - Not at all likely
2 - Slightly likely
3 - Neutral
4 - Very likely
5 - Extremely likely
✅ 7-Point Scale
1 - Not at all likely
2 - Unlikely
3 - Somewhat unlikely
4 - Neutral
5 - Somewhat likely
6 - Likely
7 - Extremely likely
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How would you rate the quality of our product?
✅ 5-Point Scale
1 - Very Poor
2 - Poor
3 - Neutral
4 - Good
5 - Excellent
✅ 7-Point Scale
1 - Extremely Poor
2 - Very Poor
3 - Poor
4 - Neutral
5 - Good
6 - Very Good
7 - Excellent
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How satisfied are you with your recent experience
with our service?
✅ 5-Point Scale
1 - Very Dissatisfied
2 - Somewhat Dissatisfied
3 - Neutral
4 - Somewhat Satisfied
5 - Very Satisfied
✅ 7-Point Scale
1 - Extremely Dissatisfied
2 - Very Dissatisfied
3 - Somewhat Dissatisfied
4 - Neutral
5 - Somewhat Satisfied
6 - Very Satisfied
7 - Extremely Satisfied
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Case study: Coca-Cola
marketing survey insights
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Source: https://www.qualtrics.com/blog/coca-cola-market-research/
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Source: https://www.qualtrics.com/blog/coca-cola-market-research/
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Source: https://www.qualtrics.com/blog/coca-cola-market-research/
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Surveys are not the only ways of learning about customers’
preferences!
“New Coke” Coca-Cola Commercial - 1985 (35 sec.):
https://www.youtube.com/watch?v=QkfFdQ1yaqs
Introducing Coca-Cola Freestyle 9100 (1.5 min.):
https://www.youtube.com/watch?v=7fPNdPFShek&t=62s
Coca-Cola Freestyle App -- Official Trailer (1 min.):
https://www.youtube.com/watch?v=QSOhzF1wiwQ&t=4s
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Read: https://www.meltwater.com/en/blog/coca-cola-consumer-insights
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Survey data analysis in
Jamovi
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Let's put theory into practice
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Step-by-step tutorial to creating a scale (survey data reduction):
1. Begin with theoretical considerations:
● What construct/variable are you interested in?
● How many dimensions does the construct represent?
2.
●
●
●
Test your assumptions statistically:
Exploratory/confirmatory factor analysis
Explore scree plot, fit indices etc.
Reliability analysis
3. Report findings:
● Cumulative % variance extracted
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Factor loadings (range)
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● Reliability: Cronbach alpha, optional: McDonald’s omega
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Good source
of publicly
available
data
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Let’s do this all in Jamovi using
OSF_data_study4.csv
Source: https://osf.io/xuamb
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Source:
https://www.google.com/search?q=
osf+datasets+customer+attitudes&
sourceid=chrome&ie=UTF-8
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Source:
https://osf.io/4scy2/
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Source: Thompson, E.
R. (2007).
Development and
validation of an
internationally
reliable short-form of
the positive and
negative affect
schedule (PANAS).
Journal of
Cross-cultural
University
Psychology, 38(2),
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227-242.
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Source: Thompson, E. R. (2007). Development and validation of an internationally reliable short-form of the positive ofGalway.ie
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and negative affect schedule (PANAS). Journal of Cross-cultural Psychology, 38(2), 227-242.
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Confirmatory factor
analysis - Interpreting
model fit:
https://stats.oarc.ucla.edu/sp
ss/seminars/introduction-tofactor-analysis/a-practical-i
ntroduction-to-factor-analy
sis-confirmatory-factor-anal
ysis/
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Reporting example:
An exploratory factor analysis (EFA) revealed [number]
factors, accounting for [percentage]% of the total variance.
The factor loadings ranged from [lower number] to [higher
number].
Cronbach's alpha for Factor 1 was [alpha1], suggesting
[interpretation based on alpha1], and for Factor 2, it was
[alpha2],
which indicates [interpretation based on alpha2].
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Questions?
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Thank you
Questions/suggestions?
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